@inproceedings{scholars17241,
             doi = {10.1109/ICFTSC57269.2022.10039913},
            note = {cited By 0; Conference of 2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022 ; Conference Date: 1 December 2022 Through 2 December 2022; Conference Code:186671},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
           title = {Investigating the Impact of Illumination and Viewpoint Variations on Transformer-based Person Re-Identification},
            year = {2022},
           pages = {130--135},
         journal = {2022 International Conference on Future Trends in Smart Communities, ICFTSC 2022},
          author = {Amosa, T. I. and Sebastian, P. and Izhar, L. I. and Ibrahim, O.},
            isbn = {9798350334548},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149140015&doi=10.1109\%2fICFTSC57269.2022.10039913&partnerID=40&md5=a65fb98c4e4c2ba333002ad6cf710834},
        abstract = {Variations in visual factors such as illumination, viewpoint, resolution, background, pose, and so on are commonly regarded as significant issues in object re-identification (re-ID). Despite widespread recognition of their importance in determining the performance of an object re-ID model, not enough attention is paid to how these factors affect re-ID systems. One of the major impediments to investigating how these factors affect the performance of re-ID models is the lack of datasets with unbiased distribution of these difficult visual conditions. To make up for the lack of large-scale datasets with a balanced distribution of such photometric and geometric transforms, recent studies suggest using game engines to generate synthetic datasets. This study proposes a quantitative investigation of the impact of two critical visual factors: illumination and Tranfomer-based re-ID models on synthetic dataset. {\^A}{\copyright} 2022 IEEE.},
        keywords = {Computer vision; Deep learning, Deep learning; Identification modeling; Illumination-adaptive; Large-scale datasets; Performance; Person re identifications; Re identifications; Synthetic datasets; Visual; Visual condition, Large dataset}
}